Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-06-07
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-06-06"3.1 state level data
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 5273 2020-06-06 New York 36 382102 30123
## 5271 2020-06-06 New Jersey 34 163893 12106
## 5262 2020-06-06 Massachusetts 25 103132 7289
## 5280 2020-06-06 Pennsylvania 42 79507 5986
## 5254 2020-06-06 Illinois 17 127251 5898
## 5263 2020-06-06 Michigan 26 64196 5894
## 5244 2020-06-06 California 6 129147 4626
## 5246 2020-06-06 Connecticut 9 43818 4055
## 5259 2020-06-06 Louisiana 22 42597 2925
## 5261 2020-06-06 Maryland 24 58099 2740
## 5249 2020-06-06 Florida 12 62750 2687
## 5277 2020-06-06 Ohio 39 38111 2370
## 5255 2020-06-06 Indiana 18 37928 2292
## 5250 2020-06-06 Georgia 13 48943 2147
## 5286 2020-06-06 Texas 48 75077 1840
## 5245 2020-06-06 Colorado 8 27834 1527
## 5290 2020-06-06 Virginia 51 49397 1460
## 5264 2020-06-06 Minnesota 27 27512 1181
## 5291 2020-06-06 Washington 53 24486 1163
## 5242 2020-06-06 Arizona 4 25517 1046
## 5274 2020-06-06 North Carolina 37 34809 1020
## 5266 2020-06-06 Missouri 29 14659 823
## 5265 2020-06-06 Mississippi 28 17034 811
## 5282 2020-06-06 Rhode Island 44 15441 772
## 5240 2020-06-06 Alabama 1 20043 689
## 5293 2020-06-06 Wisconsin 55 20701 646
## 5256 2020-06-06 Iowa 19 21527 602
## 5283 2020-06-06 South Carolina 45 13916 545
## 5248 2020-06-06 District of Columbia 11 9269 483
## 5258 2020-06-06 Kentucky 21 11359 480For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 50 counties with the largest number of deaths.
## date county state fips cases deaths
## 211368 2020-06-06 New York City New York NA 211274 21294
## 210192 2020-06-06 Cook Illinois 17031 81924 3913
## 211367 2020-06-06 Nassau New York 36059 40853 2635
## 210878 2020-06-06 Wayne Michigan 26163 21163 2627
## 209796 2020-06-06 Los Angeles California 6037 62338 2620
## 211387 2020-06-06 Suffolk New York 36103 40278 1970
## 210792 2020-06-06 Middlesex Massachusetts 25017 22686 1701
## 211293 2020-06-06 Essex New Jersey 34013 18066 1701
## 211288 2020-06-06 Bergen New Jersey 34003 18492 1612
## 211395 2020-06-06 Westchester New York 36119 33923 1523
## 211791 2020-06-06 Philadelphia Pennsylvania 42101 23529 1414
## 209895 2020-06-06 Fairfield Connecticut 9001 16020 1309
## 209896 2020-06-06 Hartford Connecticut 9003 10747 1279
## 211295 2020-06-06 Hudson New Jersey 34017 18548 1210
## 211306 2020-06-06 Union New Jersey 34039 16116 1095
## 210859 2020-06-06 Oakland Michigan 26125 10980 1055
## 211298 2020-06-06 Middlesex New Jersey 34023 16203 1032
## 209899 2020-06-06 New Haven Connecticut 9009 11817 1007
## 210788 2020-06-06 Essex Massachusetts 25009 15170 998
## 211302 2020-06-06 Passaic New Jersey 34031 16436 969
## 210796 2020-06-06 Suffolk Massachusetts 25025 18955 923
## 210846 2020-06-06 Macomb Michigan 26099 6940 870
## 210794 2020-06-06 Norfolk Massachusetts 25021 8689 859
## 210798 2020-06-06 Worcester Massachusetts 25027 11696 820
## 211301 2020-06-06 Ocean New Jersey 34029 8979 767
## 209951 2020-06-06 Miami-Dade Florida 12086 19298 765
## 211786 2020-06-06 Montgomery Pennsylvania 42091 7542 724
## 210905 2020-06-06 Hennepin Minnesota 27053 9255 667
## 210326 2020-06-06 Marion Indiana 18097 10390 663
## 210774 2020-06-06 Montgomery Maryland 24031 12662 652
## 211763 2020-06-06 Delaware Pennsylvania 42045 6661 651
## 211299 2020-06-06 Monmouth New Jersey 34025 8454 636
## 211300 2020-06-06 Morris New Jersey 34027 6584 626
## 210790 2020-06-06 Hampden Massachusetts 25013 6337 618
## 210775 2020-06-06 Prince George's Maryland 24033 16838 595
## 210795 2020-06-06 Plymouth Massachusetts 25023 8347 588
## 212436 2020-06-06 King Washington 53033 8419 578
## 211353 2020-06-06 Erie New York 36029 6429 547
## 211749 2020-06-06 Bucks Pennsylvania 42017 5243 529
## 211812 2020-06-06 Providence Rhode Island 44007 11052 518
## 210713 2020-06-06 Orleans Louisiana 22071 7222 512
## 211297 2020-06-06 Mercer New Jersey 34021 7148 500
## 209695 2020-06-06 Maricopa Arizona 4013 12761 489
## 209908 2020-06-06 District of Columbia District of Columbia 11001 9269 483
## 210786 2020-06-06 Bristol Massachusetts 25005 7635 467
## 211379 2020-06-06 Rockland New York 36087 13315 465
## 211142 2020-06-06 St. Louis Missouri 29189 5029 460
## 210703 2020-06-06 Jefferson Louisiana 22051 7831 458
## 211304 2020-06-06 Somerset New Jersey 34035 4664 425
## 212325 2020-06-06 Fairfax Virginia 51059 12056 413For these 50 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.3.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 lattice_0.20-38 nlme_3.1-144 evaluate_0.14
## [9] lifecycle_0.2.0 tibble_3.0.1 gtable_0.3.0 mgcv_1.8-31
## [13] pkgconfig_2.0.3 rlang_0.4.6 Matrix_1.2-18 yaml_2.2.1
## [17] xfun_0.12 gridExtra_2.3 withr_2.1.2 stringr_1.4.0
## [21] dplyr_0.8.4 knitr_1.28 vctrs_0.3.0 cowplot_1.0.0
## [25] grid_3.6.2 tidyselect_1.0.0 glue_1.3.1 R6_2.4.1
## [29] rmarkdown_2.1 purrr_0.3.3 farver_2.0.3 splines_3.6.2
## [33] scales_1.1.0 ellipsis_0.3.0 htmltools_0.4.0 assertthat_0.2.1
## [37] colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3 stringi_1.4.5
## [41] munsell_0.5.0 crayon_1.3.4